Automated acquisition of knowledge beyond pathologists

Abstract

1Deep learning algorithms have been successfully used in medical image classification and 2 cancer detection. In the next stage, the technology of acquiring explainable knowledge from 3 medical images is highly desired. Herein, fully automated acquisition of explainable features 4 from annotation-free histopathological images is achieved via revealing statistical distortions 5 in datasets by introducing the way of pathologists' examination into a set of deep neural 6 networks. As validation, we compared the prediction accuracy of prostate cancer recurrence 7 using our algorithm-generated features with that of diagnosis by an expert pathologist using 8 established criteria on 13,188 whole-mount pathology images. Our method found not only the 9 findings established by humans but also features that have not been recognized so far, and 10 showed higher accuracy than human in prognostic prediction. This study provides a new field 11 to the deep learning approach as a novel tool for discovering uncharted knowledge, leading to 12 effective treatments and drug discovery. 13 (149/ 150 word) 14 15

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches

“…Deep learning’s black box problem of medical image classifications has drawn remarkable attention worldwide [5]. The Group of Twenty (G20) in Osaka 2019 had referred to the importance of explainability in its declaration on artificial intelligence principles [6].…”